{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "1. Import libraries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import yfinance as yf\n",
    "from sklearn.linear_model import Ridge, Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.metrics import make_scorer"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2. Define functions to load data, create features"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def create_features(df):\n",
    "    df['Spread'] = df['High'] - df['Low']\n",
    "    df['Gap'] = df['Open'] - df['Close'].shift(1)\n",
    "    df['Intraday'] = df['Open'] - df['Close']\n",
    "    return df\n",
    "\n",
    "def drop_features(df):\n",
    "    df.drop(columns=['Spread',\n",
    "                     'Gap',\n",
    "                     'Intraday',\n",
    "                     ],\n",
    "            inplace=True)\n",
    "\n",
    "    df.drop(columns=['Open','High','Low','Close','Volume','Adj Close',\n",
    "                     ], inplace=True)\n",
    "    return df\n",
    "\n",
    "def process_features(df, lookback, step):\n",
    "    for i in range(step, lookback+1, step):\n",
    "        df['%d Spread' % (i)] = df['Spread'].pct_change(periods=i, fill_method=None)\n",
    "        df['%d Rolling Avg Spread' % (i)] = df['Spread'].rolling(window=i).mean()\n",
    "\n",
    "        df['%d Gap' % (i)] = df['Gap'].pct_change(periods=i, fill_method=None)\n",
    "        df['%d Rolling Avg Gap' % (i)] = df['Gap'].rolling(window=i).mean()\n",
    "\n",
    "        df['%d Intraday' % (i)] = df['Intraday'].pct_change(periods=i, fill_method=None)\n",
    "        df['%d Rolling Avg Intraday' % (i)] = df['Intraday'].rolling(window=i).mean()\n",
    "    return df\n",
    "\n",
    "def features(df, lookback, step):\n",
    "    create_features(df)\n",
    "    process_features(df, lookback, step)\n",
    "    drop_features(df)\n",
    "    return df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "3. Create a function to create a target."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def create_target(df, lookforward=2, target='Open'):\n",
    "    df['Target'] = np.log(df[target].shift(periods=-lookforward)/df[target].shift(periods=-1))\n",
    "    return df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "4. Create a custom scoring function."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "def custom_score(y_true, y_pred):\n",
    "  pred_sign = np.sign(y_pred)\n",
    "  y_true = np.squeeze(y_true)\n",
    "  returns = np.where((pred_sign == 1), y_true, 0)\n",
    "  return returns.mean()\n",
    "\n",
    "custom_scorer = make_scorer(custom_score, greater_is_better=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "With this scoring function, we are scoring different models on their average returns, as opposed to the normal scoring function of a regression model, which would be R^2 or RMSE, both of which are fine if we were predicting price, but accurately predicting price =/= good returns. So instead we just score the models directly on their average returns."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "5. Define the models we are going to use"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "estimator1 = Ridge()\n",
    "estimator2 = Lasso(alpha=.001)\n",
    "estimator3 = KNeighborsRegressor()\n",
    "models = [estimator1,estimator2,estimator3]\n",
    "estimator = VotingRegressor(estimators=[('Ridge', estimator1),\n",
    "                                        ('Lasso', estimator2),\n",
    "                                        ('KNN', estimator3),\n",
    "                                        ])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We combine the 3 models into 1 using the VotingRegressor. The VotingRegressor works by averaging the output of the models that its composed of into a single output. Go to this link for more info: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingRegressor.html"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "6. Define target, cross validation folds, interval, and lookback parameters."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "lookforward = 2\n",
    "tscv = TimeSeriesSplit(n_splits=5, gap=lookforward)\n",
    "steps = [1, 2, 3, 4, 5]\n",
    "lookbacks = [1, 2, 3, 4, 5]\n",
    "cv_results = pd.DataFrame(columns=['step', 'lookback', 'score'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To make sure the strategy our models come up with are actually significant, and not just a memorization of the data we gave it, we test the model 5 times (n_splits=5). We also have to add a gap between each split, otherwise there will be some overlap, which could cause the models to peek at data it shouldn't, and artificially inflate its score. Next we define the steps (or intervals) and lookbacks we want to test our model at, in order to find the ones that it scores the highest on. Lastly, we create an object to store the results of our model in."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "7. Evaluate and visualize scores of different combinations of parameters"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for step in steps:\n",
    "    for lookback in lookbacks:\n",
    "        if lookback >= step:\n",
    "            spy = yf.download('SPY', start='2004-01-01')\n",
    "            agg = yf.download('AGG', start='2004-01-01')\n",
    "\n",
    "            spy = create_target(spy, lookforward, target='Open')\n",
    "\n",
    "            spy = features(spy, lookback, step)\n",
    "            spy = spy.add_suffix(' SPY')\n",
    "            agg = features(agg, lookback, step)\n",
    "            agg = agg.add_suffix(' AGG')\n",
    "            cv = pd.merge(spy, agg, how='inner', on='Date')\n",
    "\n",
    "            cv.drop(cv.tail(lookforward).index, inplace=True)\n",
    "            cv.drop(cv.head(lookback).index, inplace=True)\n",
    "            X = cv\n",
    "            y = X[['Target SPY']]\n",
    "            X = X.drop(columns=['Target SPY'])\n",
    "            X.fillna(method=\"ffill\", inplace=True)\n",
    "            X.replace([np.inf, -np.inf], 0, inplace=True)\n",
    "            X.fillna(0, inplace=True)\n",
    "\n",
    "            cv_score = cross_val_score(estimator=estimator, X=X, y=y,\n",
    "                                       scoring=custom_scorer,\n",
    "                                       cv=tscv, verbose=0)\n",
    "\n",
    "            cv_results = cv_results.append({'step': step, 'lookback': lookback, 'score': cv_score.mean()}, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "8. Visualize results in a heatmap"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results = cv_results.pivot(index='step', columns='lookback', values='score')\n",
    "sns.heatmap(cv_results, annot=True, fmt='.6f', linewidths=.25, cmap='Blues')\n",
    "plt.title('Net Average Return')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Here we can see a 2 period step interval with a maximum look back of 2 periods performs the best. In the next notebook, we will adjust the parameters of the models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
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   "cell_type": "markdown",
   "source": [
    "BONUS: Back test best parameters"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# !pip3 install backtesting\n",
    "from backtesting import Strategy, Backtest\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "Start                     2020-01-24 00:00:00\n",
      "End                       2022-11-16 00:00:00\n",
      "Duration                   1027 days 00:00:00\n",
      "Exposure Time [%]                   99.718706\n",
      "Equity Final [$]                  1303.298889\n",
      "Equity Peak [$]                   1422.659027\n",
      "Return [%]                          30.329889\n",
      "Buy & Hold Return [%]               20.281664\n",
      "Return (Ann.) [%]                    9.843686\n",
      "Volatility (Ann.) [%]               24.604659\n",
      "Sharpe Ratio                         0.400074\n",
      "Sortino Ratio                        0.668303\n",
      "Calmar Ratio                         0.449342\n",
      "Max. Drawdown [%]                  -21.906877\n",
      "Avg. Drawdown [%]                   -3.873366\n",
      "Max. Drawdown Duration      546 days 00:00:00\n",
      "Avg. Drawdown Duration       44 days 00:00:00\n",
      "# Trades                                  709\n",
      "Win Rate [%]                         50.77574\n",
      "Best Trade [%]                       6.348897\n",
      "Worst Trade [%]                     -8.799434\n",
      "Avg. Trade [%]                        0.03753\n",
      "Max. Trade Duration           4 days 00:00:00\n",
      "Avg. Trade Duration           2 days 00:00:00\n",
      "Profit Factor                        1.099496\n",
      "Expectancy [%]                       0.048296\n",
      "SQN                                  0.728566\n",
      "_strategy                          MyStrategy\n",
      "_equity_curve                             ...\n",
      "_trades                        Size  Entry...\n",
      "dtype: object\n"
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     "execution_count": 22,
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   ],
   "source": [
    "step = 2\n",
    "lookback = 2\n",
    "\n",
    "spy = yf.download('SPY', start='2004-01-01')\n",
    "agg = yf.download('AGG', start='2004-01-01')\n",
    "\n",
    "spy = create_target(spy, lookforward, target='Open')\n",
    "\n",
    "spy = features(spy, lookback, step)\n",
    "spy = spy.add_suffix(' SPY')\n",
    "agg = features(agg, lookback, step)\n",
    "agg = agg.add_suffix(' AGG')\n",
    "cv = pd.merge(spy, agg, how='inner', on='Date')\n",
    "\n",
    "cv.drop(cv.tail(lookforward).index, inplace=True)\n",
    "cv.drop(cv.head(max(lookbacks)).index, inplace=True)\n",
    "X = cv\n",
    "y = X[['Target SPY']]\n",
    "X = X.drop(columns=['Target SPY'])\n",
    "X.fillna(method=\"ffill\", inplace=True)\n",
    "X.replace([np.inf, -np.inf], 0, inplace=True)\n",
    "X.fillna(0, inplace=True)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.85, shuffle=False)\n",
    "X_test = X_test.iloc[(abs(lookforward)):]\n",
    "y_test = y_test.iloc[(abs(lookforward)):]\n",
    "\n",
    "estimator.fit(X_train, y_train)\n",
    "forecasted = estimator.predict(X_test)\n",
    "\n",
    "data = yf.download('SPY', start='2004-01-01')\n",
    "data.drop(data.tail(lookforward).index,inplace=True)\n",
    "data.drop(data.head(lookback).index,inplace=True)\n",
    "data = data.iloc[(-X_test.shape[0]):]\n",
    "data['forecastedValue'] = forecasted\n",
    "prediction = data\n",
    "\n",
    "class MyStrategy(Strategy):\n",
    "    Data = prediction\n",
    "\n",
    "    def init(self):\n",
    "        super().init()\n",
    "\n",
    "    def next(self):\n",
    "        if self.data.forecastedValue < 0:\n",
    "            self.sell()\n",
    "        elif self.data.forecastedValue > 0:\n",
    "            self.buy()\n",
    "\n",
    "\n",
    "bt = Backtest(prediction, MyStrategy,\n",
    "              cash=1000,\n",
    "              trade_on_close=False,\n",
    "              exclusive_orders=True\n",
    "              )\n",
    "print(bt.run())\n",
    "bt.plot(show_legend=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Before we evaluate the results, one thing to mention is that the result from this back test is cheating a little bit, since we evaluated this data when we used TimeSeriesSplit. In order to make sure what we have is a real strategy, we need to remove the amount we are going to back test first, lets say 15%, and then cross validate using TimeSeriesSplit with the rest 85%, this way, our model has no way to cheat since it hasn't seen the last 15%.\n",
    "\n",
    "Onto the results, they seem to be great, but like we pointed out before, the model cheated a little bit, so in the next notebook we will take that into account and test it again after we optimize the parameters on the models and see how well it performs then."
   ],
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